Marketing is the art of reaching the right customer or consumer, with the right message at the right time. Since marketers cannot afford to craft unique messages for each targeted individual, they always deal with large segments of their target market at any given time.
Data clustering is often used to help such marketers target the appropriate segments of individuals. However, it is often difficult to determine the best clustering of a dataset. The number of possible groupings of records into clusters is extremely large. The possibilities range between all records being in a single cluster to each record being its own cluster. As a result, a comparison among alternate possible groupings of clusters belonging to the same dataset is difficult to efficiently determine. Heuristics are often used to determine which of the possible groupings of clusters is superior to alternate groupings for a particular dataset. Thus, what is needed is a system and method to help marketers in identifying the ideal number of clusters for their dataset.